Publication
Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation
Magnus Dierking; Joao Carvalho; An Thai Le; Georgia Chalvatzaki; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.20712, Pages 1-15, arXiv, 2026.
Abstract
Sampling-based Model Predictive Control (SMPC)
is a promising strategy for contact-rich robotic manipulation,
combining gradient-free optimization with massively parallel
GPU simulation. Yet, most prior work relies on simplified
dynamics or remains confined to simulation.
We present an MPC framework that leverages JAX for large-
scale parallelization and efficient computation, coupled with the
high-fidelity MuJoCo MJX simulator, and deploy it on a Franka
Research 3 executing the Push-T manipulation task through
a complete real-to-sim-to-real pipeline. The MTP variant with
structured global sampling outperforms unimodal baselines such
as CEM, MPPI, and PS across tasks that require mode switching,
both in simulation and on hardware. Furthermore, we evaluate
online domain randomization within the MPC sample budget,
showing that contact-initiation parameters yield interpretable
adaptation signals, whereas global physics parameters provide
feedback that is too weak for reliable exploitation at typical
replanning frequencies. These findings highlight key challenges
for sampling-based MPC in contact-rich manipulation—contact
sensitivity, tight compute budgets, and the difficulty of obtaining
informative domain-randomization signals in real time.